So I’ve got a bit of a confession to make: I’m a critical theory nerd. I love the philosophical debates that arise from Russian formalism versus post-structuralism, and I get a twisted masochistic enjoyment from reading Derrida’s mysticism-disguised-as-science. If I’m not reading genre fiction, odds are my nose will be buried in a critical text. But despite this guilty pleasure, it is the rare work of theory that changes how I think about the written word. But that’s exactly the kind of reaction I had to Franco Moretti’s Graphs, Maps, Trees: Abstract Models for Literary History.

A Criticism of Critical Theory and the Application of Science

Even at its most basic “Spot runs” level, the key to effective writing has always been communication. Which is why I’ve always found it mystifying that the lions of critical theory forget this basic tenet. It is a shame that in the practical world of academia, a book as lucid, well-reasoned, and communicative as Farah Mendelsohn’s impressive Rhetorics of Fantasy will spawn far fewer doctoral dissertations than the jumbled arguments of Derrida’s Of Grammatology. This just makes me sigh.

I suspect it is because my background – for the most part – lies in market research, computer sciences, statistical linguistics, economics, and mathematics. My brain is wired to work in an analytical fashion more commonly found in the hard sciences. In those fields there is zero room for the ambiguity and fuzziness present in critical theory. If a mathematician were to try to publish a paper whose equations were as muddled as the majority of critical theory texts, she would be laughed right off the top of the ivory tower. Ultimately, beneath the rhetoric of their presentation lies objective science.

However, objective need not mean uncontested or incontroversial. Consider today’s economic debates about the “right” solution to the Greek debt crisis. There’s a joke that says if you put two economists into a room, you’ll have three opinions. Yet since the early 20th century, the critical theory establishment has eschewed a rational, scientific approach to literary analysis and instead has gone down the rabbit hole of spurious semantic navel-gazing. And while that has done a lot to further the peer-reviewed publication credits of many theorists, I’d argue it hasn’t done terribly much to move our understanding of literature forward. And it also limits the critical debate to the in-crowd who grok Derrida and Foucault.

A New Formal Science for Literary Analysis: Macro versus Micro

Which is why Moretti’s Graphs, Maps, Trees is so refreshing. First, his argument has a clarity to it that most critical theorists lack. He lays out a logical case, and presents his arguments in a reasonably accessible fashion.

Fundamentally, Moretti is trying to bring the science back into critical theory. In one sense, he is updating the early 20th century’s formalism with the computational tools available to us in the 21st century. And that means that he’s mixing oil and water: words and numbers. Moretti’s underlying claim is that the close reading that forms the foundation of post-structuralism, New Criticism, most contemporary brands of Marxist criticism, etc. is a shibboleth: its propononents risk missing the forest for the trees. He argues that we can learn more about literature by applying statistical techniques across and within multiple texts. He proposes a separation between data collection and its interpretation, which is how economics, mathematics, physics, and literally every hard science in existence has operated for centuries.

A Framework for Quantitative Literary Theory

Comparing it to the dismal science (economics), Moretti’s approach is to close reading what macroeconomics is to microeconomics. Moretti argues that we now have the tools to analyze literature at a macro-level, thus enabling us to notice aspects that close reading’s micro-approach would not spot.

Perhaps unsurprisingly, this approach is controversial. Most of the folks I’ve met in the humanities self-select as “bad at math.” So a theoretical framework that relies on statistics and charts is likely to be scary: it is quite literally a new critical vocabulary, requiring an entirely different set of skills. Yet this vocabulary can be particularly compelling, and offer new insights to our understanding of genre and literature.

Moretti’s Critical Toolkit

In Graphs, Maps, Trees, Moretti explains three independent tools that can be applied to literary analysis. He devotes an entire section to each of these three techniques, and the they can even be read separately without losing much of his over-arching argument.

State of the Genre: Graphs

Of the three, the first section (graphs) is the most compelling, most understandable, and most readily applicable. A picture is worth a thousand words, and I don’t need to be in the business of data analysis (which in my day job I am) to know that graphs can communicate information more succinctly than pages of text. The statistics that Moretti employs are as simple as they get: there are no formulas, no equations, no actual math is ever shown. All Moretti does is visualize data on publication history. That’s the kind of charting we all learned back in fourth grade, but which has so rarely been applied to literature.

Once that data gets visualized, Moretti is absolutely right that certain basic trends jump out at us and demand explanation. Which is where the critical aspect comes into play. Data by its nature is an observation: it tells us “what” but not “why”. And so Moretti attempts to explain the observed behavior of the data, providing some interesting insights into the periodicity and lifespan of genres in 19th century British texts. His critical conclusions – as he himself states – are not new. Others had made similar observations before. But by visualizing an extensive set of data Moretti is able to make a stronger – less anecdotal – case. In one sense, it is like particle physicists seeking empirical proof for the Higgs-Boson. The theory supporting its existence is not new: but there’s a lot of data crunching needed to prove it.

In speculative fiction, genre fragmentation is a very real trend. We’ve got hard SF, soft SF, zombie, splatterpunk, cyberpunk, sword and sorcery, steampunk, etc. And because our minds are statistical supercomputers, we perform quantitative analyses like Moretti’s every day when we say “Vampires are so over!” or “Hard SF is dying.” We base statements like that on a fuzzy sense of what’s being published, but we generally lack the hard and fast numbers to back up such hyperbolic statements. This is just as true for critics as it is for consumers, authors, publishers, and booksellers. By looking at actual data on published texts, we can lay to rest these debates about the health of different sub-genres and perhaps identify incipient trends that are just beginning to percolate. If I were a genre publisher, or a bookseller, I would be running these kinds of analyses once a quarter to have a more scientific handle on what’s going on in the marketplace: what my competitors are publishing and what my consumers are reading. Note that this analysis has nothing to do with the quality of what is being done: merely an observation of what is happening.

State of the Book: Maps

In his second section, Moretti dives into a deeper analysis of particular texts. Rather than try to put together graphs, he draws maps based on the events, characters, and locations of the texts he is analyzing. His argument that visualizing the relationships within a book may provide us with insights into its themes and characters is extremely compelling.

Unfortunately, the science in this section of the book begins to break down. While his maps are thought-provoking, he fails to provide us with an explanation of how they were generated. In the hard sciences, nothing can be proven if a given result cannot be replicated independently. Yet Moretti fails to provide an explanation for process by which his maps were derived. Are they based on actual observed/collected data? Or are they instead conceptual diagrams meant to symbolically represent relationships within and between texts?

If the former, then a further and more precise explanation of his methods would be necessary. Such an explanation would allow other critics to replicate, test, refute, and expand on Moretti’s findings. If the latter, then a discussion of the principles and approach by which he designed the maps would also be helpful for the same reason. While this opens the door to interpretative ambiguity, it would be helpful to give other critics insight into this tool.

I would love to apply Moretti’s mapping concepts to fantasy fiction in particular. Think of the classic fantasy texts that rely so heavily on location: Alice in Wonderland, Little, Big, Peake’s Gormenghast books, or the Lord of the Rings. Speculative genres – which rely so fundamentally on world-building – are particularly conducive to this kind of analysis, and I believe we can gain much deeper insight into their themes and techniques through its application.

Relationships Between Books: Trees

In the third and final section, Moretti describes trees as a tool for analyzing the relationship between different texts. Again, this tool is less a statistical one than it is a way of visualizing large amounts of information. Essentially, trees present a certain hierarchy: they have a flow to them from one point (or set of points) to another. We’ve seen these kinds of trees many times before: flowcharts, genealogies, or folders on our computer.

But by visualizing literary works in a tree-like structure, we are able to notice relationships and trends that might otherwise get drowned out. This is particularly interesting as we examine the evolution of genres. Moretti is well aware of this, applying this technique to the mystery genre. In particular, he uses trees to visualize how Arthur Conan Doyle and his contemporary mystery writers used clues in their stories. He makes a claim that Doyle’s use of clues is why Sherlock Holmes and the rationalist mystery has survived into the present day, while his contemporary competitors have been forgotten.

His argument is compelling, and it would be far more difficult to communicate if he did not have diagrams and pictures that made it easier to follow his argument. This is another tool that I would love to see applied to speculative fiction. For example, I would love to represent the presence of invented languages in speculative fiction using these tools, and then juxtapose that against their sales statistics. Whether we learn anything that publishers, booksellers, or authors can apply is uncertain: but the results would certainly be interesting.

Doing What It Means To

At its core, Moretti’s Graphs, Maps, Trees: Abstract Models for Literary History does what it sets out to. It describes a set of techniques that can – and should – be utilized in the world of literary analysis. It shows how those techniques can be used to derive new insight into literature and genre, thus giving us a greater understanding of how written art functions.

At first glance, these techniques may seem scary. But in reality, they’re not that terrifying. Moretti’s techniques don’t use, or even any math that goes beyond an elementary school level. If he uses that kind of math, it is hidden beneath his accessible charts. If you know how to plot a simple graph, then you can begin applying his techniques. For teachers of critical theory, they offer a powerful tool to make theory accessible. Ultimately, one of Moretti’s pictures is worth ten thousand of Derrida’s words…if only because it is so easy to grasp.

From a scientific standpoint, this book is not perfect. It lacks some of the detail that would be laudable or expected in the hard sciences. But Rome was not built in a day, and had Moretti included that level of detail, I imagine that many critical theorists would be even more frightened by his ideas. I hope that more theorists – and especially genre theorists – look at Moretti’s work and try to apply some of its insights to speculative fiction.

With that in mind, here’s a short wish list of analyses I would love to see. These are really just a list of charts/diagrams that would then be wide open to interpretation and further analysis, but I think they would be really interesting and thought-provoking:

Graphs:

Number of Genre Texts Published in Hardcover vs Softcover by Sub-genre over Time

Golly…I wish I didn’t have to work for a living and had easy access to the archives of Bookscan / Amazon.com data to do even a quarter of those analyses. Anyone in the publishing industry want to pay a peer-reviewed, internationally published market researcher to put together some of this research?

As someone who’s also from a background that is part literary, part computational, I’m sympathetic to the application of informatics to the humanities. Still, I think there’s a serious limitation to data-driven studies in that we don’t really have an agreeable way to model things on the conceptual or semantic level.

An illustration: surely it’s quite neutral to track something simple like the word count of published novels over a certain period of time, perhaps even comparing it across languages, regions, or publishers—where we’re dealing with raw corpus data with no consideration of the meaning of the text. (You could say the same of what scholars used to do with assembling manual concordances of Shakespeare, Chaucer, or the Bible, only now machines have made these projects massively scalable.) But let’s say we wanted to investigate some of the questions you suggest, like comparisons between genres and subgenres. How ought we “scientifically” define those categories? What do we do with the boundary cases? We could adopt the schemes we already use to track sales, where we sort things out by publishers and imprints; if it’s from Harlequin it’s romance, if it’s from Tor it’s SF/F, and so on—but in that case we’re conferring a certain default authority to the industry’s marketing practices, and also limiting our timeframe to specific publishing models. For one thing, the concretization of genre-specific publishers simply didn’t exist at the time of HG Wells. It’s hard not to be anachronistic when we’re dealing with present categories.

I think, to a certain extent, we have to accept a degree of predetermined human rigging if we want to pursue semantic questions. Categories and tropes aren’t going to autonomously emerge from the observed data in a way that squares with our intuitions. But this isn’t all that different from the “hard” sciences, really; witness how Linnaean classification or the general schematic of a periodic table were both well in place long before we had a causal understanding of biological descent or of elements at the atomic level.

You’re absolutely right that there for the foreseeable future we’ll be dealing with some degree of manual categorization. Even with the advent of ontological fuzzy sets, I’m skeptical whether software algorithms will ever be able to adequately categorize/define genres. But that’s part of the typological fun of literary studies.